---
title: "Multi-Fact Correction in Abstractive Text Summarization (2020)"
description: "This paper presents SpanFact, a post-editing framework that repairs entity errors in any system's… 42 references, cited by 65 papers here."
authors: ["Yue Dong", "Shuohang Wang", "Zhe Gan", "Yu Cheng", "Jackie Chi Kit Cheung", "Jingjing Liu"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.749/"
doi: "10.18653/v1/2020.emnlp-main.749"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/dong-2020-multi-fact-correction-abstractive-text/"
---
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# Multi-Fact Correction in Abstractive Text Summarization

**Authors:** [Yue Dong](https://md.research.elamur.ai/free/en/authors/yue-dong/), [Shuohang Wang](https://md.research.elamur.ai/free/en/authors/shuohang-wang/), [Zhe Gan](https://md.research.elamur.ai/free/en/authors/zhe-gan/), [Yu Cheng](https://md.research.elamur.ai/free/en/authors/yu-cheng/), [Jackie Chi Kit Cheung](https://md.research.elamur.ai/free/en/authors/jackie-chi-kit-cheung/) and [Jingjing Liu](https://md.research.elamur.ai/free/en/authors/jingjing-liu/) · **Year:** 2020 · **Venue:** [Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.749/) · [PDF](https://aclanthology.org/2020.emnlp-main.749.pdf) · [DOI 10.18653/v1/2020.emnlp-main.749](https://doi.org/10.18653/v1/2020.emnlp-main.749) · [OpenAlex](https://openalex.org/W3102489149)

**Citations:** 98 citations (OpenAlex, 2026-10-14) · [65](https://md.research.elamur.ai/free/en/papers/dong-2020-multi-fact-correction-abstractive-text/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Abstractive summarizers built on pre-trained Transformers score well on ROUGE but often state facts the source does not support, especially wrong named entities. This paper presents SpanFact, a post-editing framework that repairs entity errors in any system's output without retraining the summarizer. It reuses question-answering-style span selection. The authors mask entities in reference summaries, treat the masked summary as a query and the source article as the passage, and train models to recover the missing entity spans from the document. There are two variants:
- **QA-span**: fills one mask at a time, iteratively.
- **Auto-regressive**: masks all entities together and fills them in sequence.

The correctors were applied to outputs of several summarizers on CNN/DailyMail, XSum and Gigaword. Factuality measures (QGQA, FactCC) improved substantially, ROUGE dropped only slightly, and human judges preferred the corrected summaries. This matters because earlier factuality-aware generators typically traded away much more informativeness.

## Key points
- SpanFact consists of two span-selection correctors that edit only entities, which are detected with SpaCy NER. This keeps the original summary's syntax intact.
- On XSum and Gigaword, ROUGE fell by about 0.5–1.5 points on average. The authors contrast this with 5–10-point losses reported for generation models with built-in factual control.
- QA-span works best on CNN/DailyMail, where summaries contain few errors. The auto-regressive model is better on XSum, where many hallucinated entities make single-mask context unreliable.
- In the human evaluation, AMT workers (three per pair) rated 450 pairwise samples over BertAbs, TransformerAbs and Bottom-up outputs. Corrected summaries were judged more factual than the originals more often than the reverse.
- On the FactCC human-annotated test set (508 sentences, 62 incorrect):
  - QA-span fixed 18 errors and auto-regressive fixed 16.
  - Among the 446 correct sentences, they introduced only 3 and 4 wrong entity changes, respectively.
  - The FactCC score rose from 84.89 to 86.08 (QA-span) and 85.96 (auto-regressive).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [McGill University](https://md.research.elamur.ai/free/en/universities/mcgill-university/) — University, Montreal, Canada · 51 works · 10,476 citations

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## Cite

Cite as: Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung and Jingjing Liu. 2020. Multi-Fact Correction in Abstractive Text Summarization. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{dong2020multi,
  title = {{Multi-Fact Correction in Abstractive Text Summarization}},
  author = {Yue Dong, Shuohang Wang, Zhe Gan, Yu Cheng, Jackie Chi Kit Cheung and Jingjing Liu},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.749},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.749}
}
```

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## References

42 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018. Faithful to the original: Fact aware neural abstractive summarization. In Thirty-Second AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Ziqiang+Cao%2C+Furu+Wei%2C+Wenjie+Li%2C+and+Sujian+Li%2E+2018%2E+Faithful+to+the+original%3A+Fact+aware+neural+abstractive+summarization%2E+In+Thirty%2DSecond+AAAI+Conference+on+Artificial+Intelligence%2E)
2. Sumit Chopra, Michael Auli, and Alexander M Rush. 2016. [Abstractive sentence summarization with attentive recurrent neural networks](https://md.research.elamur.ai/free/en/papers/chopra-2016-abstractive-sentence-summarization-attentive-recurrent/). In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 93–98.
3. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [Bert: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
4. Esin Durmus, He He, and Mona Diab. 2020. [Feqa: A question answering evaluation framework for faithfulness assessment in abstractive summarization](https://md.research.elamur.ai/free/en/papers/durmus-2020-feqa-question-answering-evaluation-framework/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
5. Alexander Fabbri, Irene Li, Tianwei She, Suyi Li, and Dragomir Radev. 2019. [Multi-news: A large-scale multi-document summarization dataset and abstractive hierarchical model](https://md.research.elamur.ai/free/en/papers/fabbri-2019-multi-news-large-scale-multi/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1074–1084, Florence, Italy.…
6. Tobias Falke, Leonardo FR Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019. [Ranking generated summaries by correctness: An interesting but challenging application for natural language inference](https://md.research.elamur.ai/free/en/papers/falke-2019-ranking-generated-summaries-correctness-interesting/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguisti…
7. Sebastian Gehrmann, Yuntian Deng, and Alexander M Rush. 2018. [Bottom-up abstractive summarization](https://md.research.elamur.ai/free/en/papers/gehrmann-2018-bottom-up-abstractive-summarization/). In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4098–4109.
8. Ben Goodrich, Vinay Rao, Peter J Liu, and Mohammad Saleh. 2019. Assessing the factual accuracy of generated text. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 166–175. [find](https://scholar.google.com/scholar?q=Ben+Goodrich%2C+Vinay+Rao%2C+Peter+J+Liu%2C+and+Mohammad+Saleh%2E+2019%2E+Assessing+the+factual+accuracy+of+generated+text%2E+In+Proceedings+of+the+25th+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+)
9. David Graff, Junbo Kong, Ke Chen, and Kazuaki Maeda. 2003. English gigaword. Linguistic Data Consortium, Philadelphia, 4(1):34. [find](https://scholar.google.com/scholar?q=David+Graff%2C+Junbo+Kong%2C+Ke+Chen%2C+and+Kazuaki+Maeda%2E+2003%2E+English+gigaword%2E+Linguistic+Data+Consortium%2C+Philadelphia%2C+4%281%29%3A34%2E)
10. Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. [Teaching machines to read and comprehend](https://md.research.elamur.ai/free/en/papers/hermann-2015-teaching-machines-read-comprehend/). In Advances in neural information processing systems, pages 1693– 1701.
11. Matthew Honnibal and Ines Montani. 2017. spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing. To appear, 7(1). the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). [find](https://scholar.google.com/scholar?q=Matthew+Honnibal+and+Ines+Montani%2E+2017%2E+spacy+2%3A+Natural+language+understanding+with+bloom+embeddings%2C+convolutional+neural+networks+and+incremental+parsing%2E+To+appear%2C+7%281%29%2E+the+2020+Conference+on+E)
12. Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gulcehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence rnns and beyond. In Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning, pages 280–290. [find](https://scholar.google.com/scholar?q=Ramesh+Nallapati%2C+Bowen+Zhou%2C+Cicero+dos+Santos%2C+Caglar+Gulcehre%2C+and+Bing+Xiang%2E+2016%2E+Abstractive+text+summarization+using+sequence%2Dto%2Dsequence+rnns+and+beyond%2E+In+Proceedings+of+The+20th+SIGNLL+Con)
13. Philipp Koehn and Rebecca Knowles. 2017. Six challenges for neural machine translation. In Proceedings of the First Workshop on Neural Machine Translation, pages 28–39. [find](https://scholar.google.com/scholar?q=Philipp+Koehn+and+Rebecca+Knowles%2E+2017%2E+Six+challenges+for+neural+machine+translation%2E+In+Proceedings+of+the+First+Workshop+on+Neural+Machine+Translation%2C+pages+28%E2%80%9339%2E)
14. Shashi Narayan, Shay B Cohen, and Mirella Lapata. 2018. Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1797–1807. [find](https://scholar.google.com/scholar?q=Shashi+Narayan%2C+Shay+B+Cohen%2C+and+Mirella+Lapata%2E+2018%2E+Don%E2%80%99t+give+me+the+details%2C+just+the+summary%21+topic%2Daware+convolutional+neural+networks+for+extreme+summarization%2E+In+Proceedings+of+the+2018+Con)
15. Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Evaluating the factual consistency of abstractive text summarization. arXiv preprint arXiv:1910.12840. [find](https://scholar.google.com/scholar?q=Wojciech+Krys%CC%81cin%CC%81ski%2C+Bryan+McCann%2C+Caiming+Xiong%2C+and+Richard+Socher%2E+2019%2E+Evaluating+the+factual+consistency+of+abstractive+text+summarization%2E+arXiv+preprint+arXiv%3A1910%2E12840%2E)
16. Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. 2017. Automatic differentiation in pytorch. [find](https://scholar.google.com/scholar?q=Adam+Paszke%2C+Sam+Gross%2C+Soumith+Chintala%2C+Gregory+Chanan%2C+Edward+Yang%2C+Zachary+DeVito%2C+Zeming+Lin%2C+Alban+Desmaison%2C+Luca+Antiga%2C+and+Adam+Lerer%2E+2017%2E+Automatic+differentiation+in+pytorch%2E)
17. Katherine Lee, Orhan Firat, Ashish Agarwal, Clara Fannjiang, and David Sussillo. 2018. Hallucinations in neural machine translation. [find](https://scholar.google.com/scholar?q=Katherine+Lee%2C+Orhan+Firat%2C+Ashish+Agarwal%2C+Clara+Fannjiang%2C+and+David+Sussillo%2E+2018%2E+Hallucinations+in+neural+machine+translation%2E)
18. Romain Paulus, Caiming Xiong, and Richard Socher. 2018. [A deep reinforced model for abstractive summarization](https://md.research.elamur.ai/free/en/papers/paulus-2017-deep-reinforced-model-abstractive-summarization/). In International Conference on Learning Representations.
19. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. [BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/). In Proceedings of the 58th Annual Meeting of the…
20. Haoran Li, Junnan Zhu, Jiajun Zhang, and Chengqing Zong. 2018. Ensure the correctness of the summary: Incorporate entailment knowledge into abstractive sentence summarization. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1430–1441. [find](https://scholar.google.com/scholar?q=Haoran+Li%2C+Junnan+Zhu%2C+Jiajun+Zhang%2C+and+Chengqing+Zong%2E+2018%2E+Ensure+the+correctness+of+the+summary%3A+Incorporate+entailment+knowledge+into+abstractive+sentence+summarization%2E+In+Proceedings+of+the+27)
21. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81.
22. Yang Liu and Mirella Lapata. 2019. Text summarization with pretrained encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3721–3731. [find](https://scholar.google.com/scholar?q=Yang+Liu+and+Mirella+Lapata%2E+2019%2E+Text+summarization+with+pretrained+encoders%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in+Natural+Language+Processing+and+the+9th+International+Join)
23. Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101. [find](https://scholar.google.com/scholar?q=Ilya+Loshchilov+and+Frank+Hutter%2E+2017%2E+Decoupled+weight+decay+regularization%2E+arXiv+preprint+arXiv%3A1711%2E05101%2E)
24. Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Joshua+Maynez%2C+Shashi+Narayan%2C+Bernd+Bohnet%2C+and+Ryan+McDonald%2E+2020%2E+On+faithfulness+and+factuality+in+abstractive+summarization%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Comp)
25. Cao Meng, Yue Cheung Dong, Jiapeng Wu, and Jackie Chi Kit. 2020. [Factual error correction for abstractive summarization models](https://md.research.elamur.ai/free/en/papers/cao-2020-factual-error-correction-abstractive-summarization/). In Proceedings of Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018. Improving language understanding by generative pre-training.
26. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). Journal of Machine Learning Research, 21(140):1–67.
27. Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018. [Know what you don’t know: Unanswerable questions for squad](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/). In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 784–789.
28. Alexander M Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 379–389. [find](https://scholar.google.com/scholar?q=Alexander+M+Rush%2C+Sumit+Chopra%2C+and+Jason+Weston%2E+2015%2E+A+neural+attention+model+for+abstractive+sentence+summarization%2E+In+Proceedings+of+the+2015+Conference+on+Empirical+Methods+in+Natural+Language+)
29. Abigail See, Peter J Liu, and Christopher D Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1073– 1083. [find](https://scholar.google.com/scholar?q=Abigail+See%2C+Peter+J+Liu%2C+and+Christopher+D+Manning%2E+2017%2E+Get+to+the+point%3A+Summarization+with+pointer%2Dgenerator+networks%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computation)
30. Darsh J Shah, Tal Schuster, and Regina Barzilay. 2020. Automatic fact-guided sentence modification. In Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Darsh+J+Shah%2C+Tal+Schuster%2C+and+Regina+Barzilay%2E+2020%2E+Automatic+fact%2Dguided+sentence+modification%2E+In+Proceedings+of+The+Thirty%2DFourth+AAAI+Conference+on+Artificial+Intelligence%2E)
31. Tian Shi, Ping Wang, and Chandan K. Reddy. 2019. LeafNATS: An open-source toolkit and live demo system for neural abstractive text summarization. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages 66–71, Minnea… [find](https://scholar.google.com/scholar?q=Tian+Shi%2C+Ping+Wang%2C+and+Chandan+K%2E+Reddy%2E+2019%2E+LeafNATS%3A+An+open%2Dsource+toolkit+and+live+demo+system+for+neural+abstractive+text+summarization%2E+In+Proceedings+of+the+2019+Conference+of+the+North+Ame)
32. Kaiqiang Song, Logan Lebanoff, Qipeng Guo, Xipeng Qiu, Xiangyang Xue, Chen Li, Dong Yu, and Fei Liu. 2020. Joint parsing and generation for abstractive summarization. In Proceedings of The Thirty-Fourth AAAI Conference on Artificial Intelligence, pages 4098–4109. [find](https://scholar.google.com/scholar?q=Kaiqiang+Song%2C+Logan+Lebanoff%2C+Qipeng+Guo%2C+Xipeng+Qiu%2C+Xiangyang+Xue%2C+Chen+Li%2C+Dong+Yu%2C+and+Fei+Liu%2E+2020%2E+Joint+parsing+and+generation+for+abstractive+summarization%2E+In+Proceedings+of+The+Thirty%2DFour)
33. Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman. 2017. [Newsqa: A machine comprehension dataset](https://md.research.elamur.ai/free/en/papers/trischler-2017-newsqa-machine-comprehension-dataset/). In Proceedings of the 2nd Workshop on Representation Learning for NLP, pages 191–200.
34. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In Advances in neural information processing systems, pages 5998–6008.
35. Oriol Vinyals, Meire Fortunato, and Navdeep Jaitly. 2015. Pointer networks. In Advances in neural information processing systems, pages 2692–2700. [find](https://scholar.google.com/scholar?q=Oriol+Vinyals%2C+Meire+Fortunato%2C+and+Navdeep+Jaitly%2E+2015%2E+Pointer+networks%2E+In+Advances+in+neural+information+processing+systems%2C+pages+2692%E2%80%932700%2E)
36. Oriol Vinyals and Quoc Le. 2015. [A neural conversational model](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/). arXiv preprint arXiv:1506.05869.
37. Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the factual consistency of summaries. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Alex+Wang%2C+Kyunghyun+Cho%2C+and+Mike+Lewis%2E+2020%2E+Asking+and+answering+questions+to+evaluate+the+factual+consistency+of+summaries%2E+In+Proceedings+of+the+58th+Annual+Meeting+of+the+Association+for+Comput)
38. Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier-ric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019. Huggingface’s [transformers: State-of-the-art natural language processing](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/). ArXiv, abs/1910.03771.
39. Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J Liu. 2020a. [Pegasus: Pre-training with extracted gap-sentences for abstractive summarization](https://md.research.elamur.ai/free/en/papers/zhang-2019-pegasus-pre-training-extracted-gap/). In Thirty-seventh International Conference on Machine Learning (ICML 2020).
40. Tianyi Zhang, Varsha Kishore*, Felix Wu*, Kilian Q. Weinberger, and Yoav Artzi. 2020b. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2A%2C+Felix+Wu%2A%2C+Kilian+Q%2E+Weinberger%2C+and+Yoav+Artzi%2E+2020b%2E+Bertscore%3A+Evaluating+text+generation+with+bert%2E+In+International+Conference+on+Learning+Representations%2E)
41. Chenguang Zhu, William Hinthorn, Ruochen Xu, Qingkai Zeng, Michael Zeng, Xuedong Huang, and Meng Jiang. 2020. Boosting factual correctness of abstractive summarization with knowledge graph. arXiv preprint arXiv:2003.08612. — [arXiv:2003.08612](https://arxiv.org/abs/2003.08612) [find](https://scholar.google.com/scholar?q=Chenguang+Zhu%2C+William+Hinthorn%2C+Ruochen+Xu%2C+Qingkai+Zeng%2C+Michael+Zeng%2C+Xuedong+Huang%2C+and+Meng+Jiang%2E+2020%2E+Boosting+factual+correctness+of+abstractive+summarization+with+knowledge+graph%2E+arXiv+prep)
42. CNNDM Source System Summary Corrected by SpanFact CNNDM Source System Summary Corrected by SpanFact CNNDM Source System Summary Corrected by SpanFact CNNDM Source System Summary Corrected by SpanFact CNNDM Source System Summary Corrected by SpanFact [find](https://scholar.google.com/scholar?q=CNNDM+Source+System+Summary+Corrected+by+SpanFact+CNNDM+Source+System+Summary+Corrected+by+SpanFact+CNNDM+Source+System+Summary+Corrected+by+SpanFact+CNNDM+Source+System+Summary+Corrected+by+SpanFact+)

*Updated 24 October 2026*